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Smoothing is widely used approach for measurement noise reduction in spectralanalysis. However, it suffers from signal distortion caused by peaksuppression. A locally self-adjustive smoothing method is developed thatretains sharp peaks and less distort signals. The proposed method uses only oneparameter that determines global smoothness, while balancing the localsmoothness using data itself. Simulation and real experiments in comparisonwith existing convolution-based smoothing methods indicate both qualitativelyand quantitatively improved noise reduction performance in practical scenarios.We also discuss parameter selection and demonstrate an application for theautomated smoothing and detection of a given number of peaks from noisymeasurement data.

Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection